Finding the top scoring neighbors for the Dot Product decoder at the sublinear time complexity is a well studied research problem, known as the approximate maximum inner product search (MIPS).
There are several approaches to MIPS: sampling based [1]}, [2]}, [3]}, LSH-based [4]}, [5]}, [6]}, [7]}, graph based [8]}, [9]}, [10]}, and quantization approaches [11]}, [12]}.
MIPS is a fundamental building block in various application domains [13]}, [14]}, [15]}, [16]}, [17]}, [18]}, such as information retrieval [19]}, [20]}, pattern recognition [21]}, [22]}, data mining [23]}, [24]}, machine learning [25]}, [26]}, and recommendation systems [27]}, [28]}.
